A probabilistic generative model for GO enrichment analysis

被引:59
作者
Lu, Yong [1 ]
Rosenfeld, Roni [2 ]
Simon, Itamar [3 ]
Nau, Gerard J. [4 ]
Bar-Joseph, Ziv [1 ,2 ]
机构
[1] Carnegie Mellon Univ, Dept Comp Sci, Pittsburgh, PA 15213 USA
[2] Carnegie Mellon Univ, Machine Learning Dept, Pittsburgh, PA 15213 USA
[3] Hebrew Univ Jerusalem, Sch Med, Dept Mol Biol, IL-91120 Jerusalem, Israel
[4] Univ Pittsburgh, Sch Med, Dept Mol Genet & Biochem, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会;
关键词
D O I
10.1093/nar/gkn434
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The Gene Ontology (GO) is extensively used to analyze all types of high-throughput experiments. However, researchers still face several challenges when using GO and other functional annotation databases. One problem is the large number of multiple hypotheses that are being tested for each study. In addition, categories often overlap with both direct parents/descendents and other distant categories in the hierarchical structure. This makes it hard to determine if the identified significant categories represent different functional outcomes or rather a redundant view of the same biological processes. To overcome these problems we developed a generative probabilistic model which identifies a (small) subset of categories that, together, explain the selected gene set. Our model accommodates noise and errors in the selected gene set and GO. Using controlled GO data our method correctly recovered most of the selected categories, leading to dramatic improvements over current methods for GO analysis. When used with microarray expression data and ChIP-chip data from yeast and human our method was able to correctly identify both general and specific enriched categories which were overlooked by other methods.
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页数:10
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